Real-time attack detection on robot cameras: A self-driving car application

International audienceThe Robot Operating System (ROS) are being deployed for multiple life critical activities such as self-driving cars, drones, and industries. However, the security has been persistently neglected, especially the image flows incoming from camera robots. In this paper, we perform a structured security assessment of robot cameras using ROS. We points out a relevant number of security flaws that can be used to take over the flows incoming from the robot cameras. Furthermore, we propose an intrusion detection system to detect abnormal flows. Our defense approach is based on images comparisons and unsupervised anomaly detection method. We experiment our approach on robot cameras embedded on a self-driving car

Fast Indexing Strategies for Robust Image Hashes

Similarity preserving hashing can aid forensic investigations by providing means to recognize known content and modified versions of known content. However, this raises the need for efficient indexing strategies which support the similarity search. We present and evaluate two indexing strategies for robust image hashes created by the ForBild tool. These strategies are based on generic indexing approaches for Hamming spaces, i.e. spaces of bit vectors equipped with the Hamming distance. Our first strategy uses a vantage point tree, and the second strategy uses locality-sensitive hashing (LSH). Although the calculation of Hamming distances is inexpensive and hence challenging for indexing strategies, we improve the speed for identifying similar items by a factor of about 30 with the tree-based index, and a factor of more than 100 with the LSH index. While the tree-based index retrieves all approximate matches, the speed of LSH is paid with a small rate of false negatives